Itamar Turner Trauring
where else to find them
website:https://pythonspeed.com
github:itamarst
mastodon:@itamarst@hachyderm.io
x:itamarst
linkedin:itamar-turner-trauring
devto:itamarst
what they talk about
all 7 in the filters
where they keep going back
- PyCon US3 editions2020–2025
- PyData Global2 editions2022–2023
all 8 in the filters
on a stage with
never shared a talk
who talks about the same things
- Francesc Alted10 talks
- Ian Ozsvald17 talks
- Matt Harrison16 talks
- Dharhas Pothina5 talks
- Ondrej Kokes3 talks
- Matthew Rocklin13 talks
| title | conference | date |
|---|---|---|
| Processing large JSON files without running out of memory | PyData Boston 2025 | 2025-12-08 |
Loading a 50 MB JSON file can use ~230 MB of RAM (about 4.5×) and a 10 GB file can demand ~40 GB, so peak memory is the limiter not average. Four practical… | ||
| Processing large JSON files without running out of memory | PyCon US 2025 | 2025-05-16 |
Shows concrete numbers and tools: json.load -> 50 MB file becomes ~200 MB (4x) measured with user/bin/time, resource and tracemalloc. Demonstrates three fixes… | ||
| Optimize first, parallelize second: a better path to faster data processing | PyData Global 2023 | 2023-12-06 |
If code is slow, the decisive details are what actually changes performance: the measured speedup numbers, the algorithmic complexity fixed, the architecture… | ||
| Vectorization: How slow Python runs fast code | Python Web Conf 2023 | 2023-03-13 |
Vectorization means using chunked numeric representations and bulk native routines so Python code runs C/Rust-speed on large arrays. Using numpy.sum on a 30… | ||
| Speed up Python data processing with vectorization | PyData Global 2022 | 2022-12-01 |
Vectorization means using array-backed APIs to bypass per-item Python overhead and get order-of-magnitude speedups. Using NumPy and Pandas, a Python for loop… | ||
| Zero to production-ready: a best-practices process for Docker packaging | Docker Community All Hands #5 (March 2022) | 2022-03-31 |
Gives a prioritized, iterative process: get something running, then fix security, add automated builds, ensure reproducible pinning and only then optimize for… | ||
| Zero to production ready: a best practices process for Docker packaging | DjangoCon US 2021 | 2021-10-22 |
Starts with a working image and an explicit stepwise process: python:3.9-slim-bullseye base, listen on 0.0.0.0, non-root user and apt-get upgrade for security… | ||
| 0 to production-ready: a best-practices process for Docker packaging | PyCon US 2021 | 2021-05-14 |
A concrete iterative process appears: first get a working image, then fix security (for example don't run as root and remove build-time secrets), then automate… | ||
| Small Big Data: using NumPy and Pandas | Data Love 2021 | 2021-04-16 |
Shows how to process datasets larger than RAM on a single machine using concrete NumPy and Pandas techniques, not cluster handwaving. Explains that running out… | ||
| Small Big Data: using NumPy and Pandas when your data doesn't fit ... | PyCon US 2020 | 2020-04-15 |
Explains concrete tactics to handle data too big for RAM using NumPy and Pandas: lossless dtype downcasting (64→16-bit cuts memory ~4x), sparse arrays for… | ||
| Small Big Data: using NumPy and Pandas when your data... | PyData New York City 2019 | 2019-11-04 |
Concrete tactics and numbers: shows how NumPy and Pandas handle data that won’t fit in RAM using compression, chunking, indexing and cheap extra memory, with… | ||
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